Pavlova Maya, Terhljan Naomi, Chung Audrey G, Zhao Andy, Surana Siddharth, Aboutalebi Hossein, Gunraj Hayden, Sabri Ali, Alaref Amer, Wong Alexander
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
Waterloo AI Institute, University of Waterloo, Waterloo, ON, Canada.
Front Med (Lausanne). 2022 Jun 10;9:861680. doi: 10.3389/fmed.2022.861680. eCollection 2022.
As the COVID-19 pandemic devastates globally, the use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow given its routine clinical use for respiratory complaint. As part of the COVID-Net open source initiative, we introduce COVID-Net CXR-2, an enhanced deep convolutional neural network design for COVID-19 detection from CXR images built using a greater quantity and diversity of patients than the original COVID-Net. We also introduce a new benchmark dataset composed of 19,203 CXR images from a multinational cohort of 16,656 patients from at least 51 countries, making it the largest, most diverse COVID-19 CXR dataset in open access form. The COVID-Net CXR-2 network achieves sensitivity and positive predictive value of 95.5 and 97.0%, respectively, and was audited in a transparent and responsible manner. Explainability-driven performance validation was used during auditing to gain deeper insights in its decision-making behavior and to ensure clinically relevant factors are leveraged for improving trust in its usage. Radiologist validation was also conducted, where select cases were reviewed and reported on by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed that the critical factors leveraged by COVID-Net CXR-2 are consistent with radiologist interpretations.
随着新冠疫情在全球肆虐，鉴于胸部X光（CXR）成像在呼吸系统疾病临床常规应用中的作用，其作为实时荧光定量聚合酶链反应（RT-PCR）检测的辅助筛查策略，应用持续增加。作为新冠网络开源计划的一部分，我们推出了新冠网络CXR-2，这是一种增强型深度卷积神经网络设计，用于从CXR图像中检测新冠病毒，它所使用的患者数量更多、种类更丰富，超过了原始的新冠网络。我们还引入了一个新的基准数据集，该数据集由来自至少51个国家的16,656名患者的19,203张CXR图像组成，使其成为开放获取形式下最大、最多样化的新冠病毒CXR数据集。新冠网络CXR-2网络的灵敏度和阳性预测值分别达到95.5%和97.0%，并以透明且负责的方式进行了审核。审核过程中采用了可解释性驱动的性能验证，以更深入了解其决策行为，并确保利用临床相关因素来提高对其使用的信任度。还进行了放射科医生验证，分别由两位具有10年和19年以上经验的董事会认证放射科医生对选定病例进行审查和报告，结果表明新冠网络CXR-2所利用的关键因素与放射科医生的解读一致。
